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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30763 条记录，以下是171-180 订阅

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Proceedings - 2022 ieee 42nd international conference on distributed computing systems Workshops, ICDCSW 2022

Proceedings - 2022 IEEE 42nd International Conference on Dis...

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42nd ieee international conference on distributed computing systems Workshops, ICDCSW 2022

ISBN: (纸本)9781665488792

The proceedings contain 54 papers. The topics discussed include: a distributed energy trading framework with secure and effective consensus protocol;stealthy clustering of Ethereum transactions using time leakage from fixed nodes;a blockchain-based profiling system for exploring human factors in cloud-edge-end orchestration;local model quality control method based on credit mortgage for enterprise credit evaluation;Holium: a protocol for data transformation pipelines;fair incentivization of bandwidth sharing in decentralized storage networks;hierarchical consensus: a horizontal scaling framework for blockchains;a graph diffusion scheme for decentralized content search based on personalized PageRank;towards efficient decentralized federated learning;towards portable identities in the matrix protocol;and flash freezing flash boys: countering blockchain front-running.

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Proceedings - 2024 ieee 44th international conference on distributed computing systems, ICDCS 2024

Proceedings - 2024 IEEE 44th International Conference on Dis...

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44th ieee international conference on distributed computing systems, ICDCS 2024

ISBN: (纸本)9798350386059

The proceedings contain 151 papers. The topics discussed include: reducing first-frame delay of live streaming by simultaneously initializing window and rate;privacy-preserving finger movement tracking using acoustic sensing enhanced by smartphone case mini-structures;a performance analysis modeling framework for extended reality applications in edge-assisted wireless networks;LinuxFP: transparently accelerating Linux networking;edge-assisted relevance-aware perception dissemination in vehicular networks;CoCFL: a lightweight blockchain-based federated learning framework in IoT context;Hammer: a general blockchain evaluation framework;DNN partitioning and assignment for distributed inference in SGX empowered edge cloud;and towards integrated energy-communication-transportation hub: a base-station-centric design in 5G and beyond.

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Proceedings - 2023 ieee international conference on High Performance computing and Communications, Data Science and systems, Smart City and Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2023

Proceedings - 2023 IEEE International Conference on High Per...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

ISBN: (纸本)9798350330014

The proceedings contain 156 papers. The topics discussed include: integrating machine learning and social sensing in smart city digital twin for citizen feedback;a novel fuzzy identity-based encryption with wireless PUF for IoT device authentication;RDA-Unet: a retrieval model for radar composite reflectivity factor from Himawari-8 observations;DHIFS: a dynamic and hybrid index method with low memory overhead and efficient file access;multi-agent reinforcement learning for smart city automated traffic light control;ST-ConvLSGRU: a radar echo extrapolation method based on LSTM and GRU;intelligent maintenance system for wind turbine based on deep belief federated learning;BioNet+: a comprehensive interaction model of finding therapeutics for diseases with graph deep learning;and PBE-Plan: periodic backdoor erasing plan for trustworthy federated learning.

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GCN-Integrated Multi-Agent Reinforcement Learning (MARL) with Modified Firefly Algorithm for Cross-Layer Optimization in Cognitive sensor Networks

GCN-Integrated Multi-Agent Reinforcement Learning (MARL) wit...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Karthik Jakranpally Nagabathula Ramesh Babu Doddavarapu Sreenu Babu K. Rakesh Amit Barve Atmaram Shelke Valiant IT Services4000 Stanwick ave Sacramento CA Department of Electronics and Communication Engineering J.B. Institute of Engineering & Technology Hyderabad India Department of Electronics and Communication Engineering Bharatratna Indira Gandhi College of Engineering Solapur India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University P.O.Limda Gujarat India Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

CSN s are integrating cognitive abilities, making it challenging to achieve optimized cross-layer performance with limited resources. Non-coordinated techniques typically demonstrate lower proficiency in dynamic spectrum management and network synchronization. We introduce Graph Convolutional Networks (GCN) and Multi-Agent Reinforcement Learning (MARL) in our architectural solution, with the help of the specially designed Firefly Algorithm. The framework implements a distributed learning process, utilizing GCN for local topology perception and MARL for cooperative spectrum and optimal routing decisions. The results of experimental analysis show large gains compared to traditional methods. The proposed model provided a 94.2% recognition rate in spectrum hole identification, an 89.7% enhancement in network throughput, and a 78.5% diminution in end-to-end delay. The energy efficiency surpasses baseline techniques by 67.3%, while the raw packet delivery reliability remains at 92.8%. Integrating GCN with MARL and the improved Firefly Algorithm can significantly improve cross-layer optimization, offering a promising solution for the next generation of sensor networks.

关键词： Cross layer design Graph convolutional networks Reinforcement learning Throughput Routing Topology Synchronization Reliability Optimization Radio spectrum management

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A Secure Framework for Authenticating and Controlling Access to IoT Devices Integrated with Blockchain

A Secure Framework for Authenticating and Controlling Access...

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ieee international conference on Consumer Electronics (ICCE)

作者： Sibgha Tahir Noshina Tariq Farrukh Aslam Khan Hilmand Khan Jalal Al-Muhtadi Department of Avionics Engineering Air University Islamabad Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan Center of Excellence in Information Assurance King Saud University Riyadh Saudi Arabia Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The Internet of Things (IoT) enables autonomous communication among devices, improving human life but posing security risks. The main challenges to IoT systems' security are unauthorized access, data tampering, and the inability of central access control mechanisms to cope with scalability and privacy. Therefore, robust authentication and access control mechanisms are crucial for secure data sharing across IoT devices. This paper combines Hyperledger Fabric (HLF) blockchain with IoT for enhanced access control to establish trust in authentication and identification of devices along with secure communication. Hyperledger components are implied for developing an Attribute-Based Access Control (ABAC) model, including certification authorities, endorsers, and membership service providers. The system is implemented using HLF source code, Docker images, and IBM Cloud Kubernetes services, with an interface via Node-Red for device registration, authentication, blockchain data storage, and ledger retrieval. Experimental results demonstrate effective attack mitigation and better efficiency in terms of time complexity and energy consumption.

关键词： Access control Cloud computing Power demand distributed ledger Authentication Fabrics Blockchains Delays Internet of Things Time complexity

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Energy-Efficient Wireless sensor Networks Optimization using Deep Q-Networks for Smart Energy Applications

Energy-Efficient Wireless Sensor Networks Optimization using...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Satyanarayana Nimmala Nukala Sujata Gupta P Vasanth Sena K Koteswara Chari Md Aleem Pasha Bandi Rambabu Department of CSE (Data Science) CVR College of Engineering Hyderabad India Department of Computer Engineering Technology Chaitanya Bharati Institute of Technology (CBIT) Hyderabad India Department of CSE G. Narayanamma Institute of Technology and Science Hyderabad India Department of CSE Teegala Krishna Reddy Engineering College Hyderabad India Department of Mechanical Engineering Chaitanya Bharati Institute of Technology (CBIT) Hyderabad India Department of CSE CVR College of Engineering Hyderabad India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Energy efficiency poses a significant difficulty in Wireless sensor Networks (WSNs), especially within smart energy applications like smart grids, energy monitoring, and predictive maintenance. This study presents an adaptive optimization approach based on reinforcement learning utilizing Deep Q-Networks (DQN) to improve energy efficiency in Wireless sensor Networks (WSNs) through the dynamic adjustment of transmission power and routing algorithms. The suggested model is trained and assessed utilizing the LEACH (Low-Energy Adaptive Clustering Hierarchy) dataset within a simulated NS-3 and MATLAB environment. The simulation configuration comprises 100 to 500 sensor nodes distributed inside a 100m x 100m region, each with differing energy limitations, utilizing the ieee 802.15.4(Zigbee) communication protocol. The performance evaluation is carried out under varying traffic loads and network density, contrasting the proposed DQN-based optimization with classic LEACH, GA-based clustering, and Q-learning models. The results indicate a 27.5% enhancement in network longevity relative to LEACH, a 21.8% decrease in energy consumption per node, an 18.3% advancement in packet delivery ratio, and a 16.7% reduction in end-to-end latency, hence providing dependable data transfer. The findings underscore the efficacy of reinforcement learning in prolonging the lifespan of wireless sensor networks and enhancing energy efficiency, rendering it exceptionally appropriate for practical smart energy applications, including power grid monitoring, industrial IoT, and renewable energy management. The proposed DQN-based optimization dynamically adjusts transmission power and routing strategies, resulting in significant energy savings and improved network performance

关键词： Wireless sensor networks Adaptive systems Heuristic algorithms Telecommunication traffic Routing Energy efficiency Mathematical models Smart grids Optimization Monitoring

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Proceedings - 2015 ieee 35th international conference on distributed computing systems Workshops, ICDCSW 2015

Proceedings - 2015 IEEE 35th International Conference on Dis...

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2015 35th ieee international conference on distributed computing systems Workshops, ICDCSW 2015

ISBN: (纸本)9781467373036

The proceedings contain 23 papers. The topics discussed include: on notification message re-broadcasting for the node-disjoint multipath routing method in ad hoc networks to counter eavesdropping of data packets;an alloy verification model for consensus-based auction protocols;a proposal for mobile collaborative work support platform using an embedded data stream management system;using genetic programming to identify tradeoffs in self-stabilizing programs: a case study;ensuring cloud service guarantees via service level agreement (SLA)-based resource allocation;Tengu: an experimentation platform for big data applications;making WiFi work in multi-hop topologies: automatic negotiation and allocation of airtime;expressing different traffic models using the LegoTG framework;providing a high level abstraction for SDN networks in GENI;in-network dynamic pathlet switching with VIRO for SDN networks;and selection of virtual machines based on classification of MapReduce jobs.

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Edge AI in 6G: Vision, Technologies, and Applications

Edge AI in 6G: Vision, Technologies, and Applications

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Pervasive Computational Technologies (ICPCT), international conference on

作者： Vanessa Azucena Monzón Centeno Jesús Emilio Agustín Padilla Caballero Claudia Rossana Poma García Jonathan Orihuela Flores Wilmer Amado Huamán Malca Carlos Alberto Ferrando Gómez Escuela de Posgrado Universidad César Vallejo Lima Perú Pregrado Universidad Nacional de Educación Enrique Guzmán y Valle Lima Perú Pregrado Universidad Nacional del Callao Callao Perú

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The development of wireless connections is being pushed by an unprecedented rise of artificial intelligence (AI) software, with 6G becoming a game-changing technology. However advanced artificial intelligence (AI) needs a lot of connectivity and processing power, which causes delays, energy waste, network traffic, and privacy breaches. By combining sensing, interaction, computing, and intellect, Edge AI—which incorporates model learning and reasoning capacities at the connection's edge—offers an innovative approach for 6G network deployments. With an emphasis on distributed machine learning algorithms and incorporated wireless networking protocols, this article offers a vision for sustainable and reliable edge artificial intelligence (AI) systems. To fit edge AI, the article also covers novel design concepts, service-driven utilization techniques for optimizing, and a complete end-to-end system framework.

关键词： 6G mobile communication Wireless communication Wireless sensor networks Edge AI Telecommunication traffic Software sensors Reliability Internet of Things Artificial intelligence

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingyu Liu Yijie Wang Xiaohui Zhou Yongjun Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors.

关键词： Learning systems Time-frequency analysis Fast Fourier transforms Time series analysis Signal processing Feature extraction Entropy Iterative methods Speech processing Anomaly detection

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Artificial Intelligence based Anomaly detection in distributed energy resources for Smart Grid

Artificial Intelligence based Anomaly detection in Distribut...

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Shradha Umathe Neelima Dudhe Rutuja Hiware Prajakta Vaidya Department of Electrical Engineering G H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The rapid emergence and evolution of Information and Communication Technologies (ICT) has greatly impacted the energy distribution industry. This has led to the transformation of the traditional distribution system into a smart grid. Although the design strategies for smart grids have been studied by academic researchers, there is currently no practical guidance available for the industry on how to improve the level of intelligence in the grid. An industrial control system (ICS) is a type of device that monitors and controls the activities of an industrial environment. Unlike other forms of communication, the traffic of ICS is stable, regular, and periodical. Through statistical modeling, we can analyze the various features of this communication to create a statistical profile of its performance. For instance, by observing the inter-arrival and packet direction of this communication, we can identify the distribution of features that are useful in analyzing the communication. Due to the complexity of the issue, many researchers are currently working on developing techniques to detect anomalous intrusions in smart grids. Unfortunately, there is not enough experience in this field to develop effective approaches. Another issue that emerges when implementing pattern recognition techniques is that it can be very challenging to identify the signals. Due to the nature of the data in smart grids, there are often high levels of misclassification. The ability to detect and prevent power outages is very important for the safety and operation of the power grid. Modern technologies have evolved to provide a large and complex cyber physical system (CPS) that can monitor and control the various components of the power grid. The power grid is continuously improving due to the advancements in technology, and the number of sensors and measurement devices that can be used to monitor and control the system will increase. Unfortunately, this increase in data can overwhelm the existing S

关键词： Industries SCADA systems sensor systems Smart grids Information and communication technology Safety Security Monitoring Anomaly detection Random forests

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